CN111541248A - Intelligent soft switch and energy storage system combined optimization method and device - Google Patents

Intelligent soft switch and energy storage system combined optimization method and device Download PDF

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CN111541248A
CN111541248A CN202010529126.3A CN202010529126A CN111541248A CN 111541248 A CN111541248 A CN 111541248A CN 202010529126 A CN202010529126 A CN 202010529126A CN 111541248 A CN111541248 A CN 111541248A
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energy storage
storage system
intelligent soft
soft switch
model
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CN111541248B (en
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徐全
袁智勇
雷金勇
林跃欢
史训涛
白浩
徐敏
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CSG Electric Power Research Institute
China Southern Power Grid Co Ltd
Research Institute of Southern Power Grid Co Ltd
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Research Institute of Southern Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The application discloses a method and a device for jointly optimizing an intelligent soft switch and an energy storage system, wherein a deterministic joint optimization model of the intelligent soft switch and the energy storage system of a power distribution system is constructed based on acquired system parameters; performing second-order cone model conversion on the deterministic combined optimization model of the intelligent soft switch and the energy storage system to obtain a deterministic second-order cone planning model of the intelligent soft switch and the energy storage system; on the basis of a configured uncertain set of distributed power output and load demand prediction and a deterministic second-order cone planning model of the intelligent soft switch and energy storage system, an optimization model of an intelligent soft switch and energy storage system combined interval is constructed and solved to obtain an optimization result, and the technical problems that the existing intelligent soft switch operation optimization method aims at deterministic distributed power output and load demand prediction information, the uncertain factors are less involved in processing, impact caused by the uncertain factors cannot be coped with, and the operation safety of a power distribution system is low are solved.

Description

Intelligent soft switch and energy storage system combined optimization method and device
Technical Field
The application relates to the technical field of power distribution system optimization, in particular to a method and a device for jointly optimizing an intelligent soft switch and an energy storage system.
Background
As an important link connecting a power supply and a load, the economic efficiency and safety of a power distribution system directly affect the benefits of power operators and the power quality of power users, along with the development of energy storage technology and the popularization of electronic devices, the number of available resources in the power distribution system is increasing, and the operation control problem of the power distribution system becomes more complicated. Meanwhile, along with the high-penetration access of the distributed power supply, the power distribution system faces a series of new problems such as bidirectional power flow, voltage out-of-limit, network blockage and the like due to the fact that the operation characteristics of the distributed power supply are greatly influenced by the environment and have strong randomness and volatility and the introduction of a large number of uncertain factors. Therefore, various schedulable devices such as an energy storage system and an intelligent soft switch need to be orderly coordinated, the operation optimization potential of each device is fully exerted, the challenges brought by uncertainty factors are met, and the safe and efficient operation of the power distribution system is realized.
The active power distribution system can realize fine power flow control, auxiliary measurement and communication equipment improvement of system reliability by means of power electronic equipment, so that the running state of the power distribution system is optimized. An intelligent soft Switch (SOP) is a novel power distribution device based on power electronic elements, and when a power distribution system normally operates, the intelligent soft switch can adjust transmission power between feeders in real time and optimize a network operation state. In the actual operation process, the configuration of a measurement terminal in a power distribution system is less, the reliability of a communication network is poorer, and a certain deviation can be introduced into the algorithm of the existing prediction method based on intelligent optimization algorithms such as a neural network, so that the accurate prediction of parameters such as the output of a distributed power supply, the load demand and the like is more difficult. Therefore, an intelligent soft switch operation optimization method for the active power distribution system, which can fully consider the uncertainty of the distributed power supply and the load, is urgently needed.
At present, the existing intelligent soft switch operation optimization method aims at deterministic distributed power output and load demand prediction information, less relates to processing of uncertain factors, and cannot cope with impact caused by the uncertain factors, so that the operation safety of a power distribution system is low.
Disclosure of Invention
The application provides a combined optimization method and a combined optimization device for an intelligent soft switch and an energy storage system, which are used for solving the technical problems that the existing operation optimization method for the intelligent soft switch is mainly used for predicting the output of a deterministic distributed power supply and load demand, less relates to the treatment of uncertain factors, and cannot cope with the impact caused by the uncertain factors, so that the operation safety of a power distribution system is low.
In view of the above, a first aspect of the present application provides a method for jointly optimizing an intelligent soft switch and an energy storage system, including:
acquiring system parameters of a power distribution system;
constructing a deterministic combined optimization model of the intelligent soft switch and the energy storage system of the power distribution system based on the system parameters;
performing second-order cone model conversion on the deterministic combined optimization model of the intelligent soft switch and the energy storage system to obtain a deterministic second-order cone planning model of the intelligent soft switch and the energy storage system of the power distribution system;
configuring an uncertain set of the output and load demand prediction of the distributed power supply, and constructing an intelligent soft switch and energy storage system combined interval optimization model based on the intelligent soft switch and energy storage system deterministic second-order cone programming model and the uncertain set;
and solving the optimization model of the intelligent soft switch and energy storage system combined interval to obtain an optimization result, wherein the optimization result at least comprises an energy storage system day-ahead scheduling strategy and an intelligent soft switch day-in-time operation strategy.
Optionally, the system parameters include: the system comprises line parameters, load levels, network topology connection relations, installation positions and capacities of distributed power supplies, installation positions and capacities of energy storage systems, system operation voltage levels and branch circuit current limits, access positions of intelligent soft switches, configuration capacities and loss coefficients, a day-ahead load demand prediction curve, a day-ahead distributed power supply output prediction curve, time-of-use electricity price parameters, uncertain regulation coefficients and uncertain deviations, and initial values of system reference voltages and reference powers.
Optionally, an objective function of the deterministic combined optimization model of the intelligent soft switch and the energy storage system is as follows:
Figure BDA0002534726970000021
wherein N istNumber of time sections, omegabIs the set of all branches of the distribution system, rijIs the resistance value of branch ij, It,ijFor the current amplitude of branch ij at time t,
Figure BDA0002534726970000022
for the loss of the intelligent soft switch installed on branch ij at time t,
Figure BDA0002534726970000023
the price of electricity at time t.
Optionally, the constraint conditions of the objective function include: the system comprises a network topology constraint, a system power flow constraint, a distributed power supply operation constraint, a power distribution system operation constraint, an intelligent soft switch operation constraint and an energy storage system operation constraint.
Optionally, the deterministic second-order cone planning model of the intelligent soft switch and the energy storage system of the power distribution system is as follows:
Figure BDA0002534726970000031
wherein, x: ═ Pch,Pdis)TFor the operating strategy of the energy storage system, y: ═ (P)SOP,QSOP)TIn order to realize the operation strategy of the intelligent soft switch,
Figure BDA0002534726970000032
NNnumber of nodes of power distribution system, NtThe number of the time sections is the number of the time sections,
Figure BDA0002534726970000033
Figure BDA0002534726970000034
respectively the charging power and the discharging power of the energy storage system on the node i at the time t,
Figure BDA0002534726970000035
Figure BDA0002534726970000036
Figure BDA0002534726970000037
respectively, the active power and the reactive power of the intelligent soft switch on the node i at the time t, A, C, D, G, H are respectively system matrixes of the model, and z: (U: ═ is2,I2,P,Q,V)TFor power flow control variables, U2:＝(U2,t,i,t＝1,2,…Nt,i＝1,2,…,NN)，U2,t,iIs the square of the magnitude of the voltage at node I at time t, I2:＝(I2,t,i,j,t＝1,2,…Nt,i,j＝1,2,…,NN)，I2,t,ijIs the square of the current magnitude on branch ij at time t, P: ═ Pt,i,t＝1,2,…,Nt,i＝1,2,…,NN)，Q:＝(Qt,i,t＝1,2,…,Nt,i＝1,2,…,NN)，Pt,i、Qt,iThe active power and the reactive power injected at the node i at the time t, respectively, wherein: ═ Vt,i,t＝1,2,…,Nt,i＝1,2,…,NN)，Vt,iB, c, e, f, g are the coefficient vectors of the model respectively,
Figure BDA0002534726970000038
for active power prediction of distributed power sources and loads,
Figure BDA0002534726970000039
Figure BDA00025347269700000310
for the active power prediction value of the distributed power supply at the node i at the time t,
Figure BDA00025347269700000311
Figure BDA00025347269700000312
and the predicted value of the active power of the load on the node i at the moment t is obtained.
Optionally, the uncertain set of the distributed power output and load demand prediction is as follows:
Figure BDA00025347269700000313
wherein the content of the first and second substances,
Figure BDA00025347269700000314
respectively the actual value of the active power of the distributed power supply at the node i at the time t and the actual value of the active power of the load,
Figure BDA00025347269700000315
the deviations introduced by the distributed power supply and the uncertain load variation range on the node i at the time t respectively,DG、Lcorresponding uncertainty adjustment for distributed power and load respectivelyAnd (4) saving parameters.
Optionally, the solving the optimization model of the combined interval of the intelligent soft switch and the energy storage system to obtain an optimization result includes:
decoupling the intelligent soft switch and the energy storage system joint interval optimization model to obtain a main problem model and a sub problem model;
and solving the main problem model and the sub problem model based on a column and constraint generation algorithm to obtain an optimization result.
This application second aspect provides an intelligence soft switch and energy storage system jointly optimize device, includes:
the acquisition unit is used for acquiring system parameters of the power distribution system;
the first construction unit is used for constructing a deterministic combined optimization model of the intelligent soft switch and the energy storage system of the power distribution system based on the system parameters;
the conversion unit is used for performing second-order cone model conversion on the intelligent soft switch and energy storage system certainty combined optimization model to obtain an intelligent soft switch and energy storage system certainty second-order cone planning model of the power distribution system;
the second construction unit is used for configuring an uncertain set of the output and load demand prediction of the distributed power supply and constructing an intelligent soft switch and energy storage system joint interval optimization model based on the intelligent soft switch and energy storage system deterministic second-order cone planning model and the uncertain set;
and the solving unit is used for solving the optimization model of the intelligent soft switch and energy storage system combined interval to obtain an optimization result, and the optimization result at least comprises a day-ahead scheduling strategy and an intelligent soft switch day-in-day operation strategy of the energy storage system.
Optionally, an objective function of the deterministic combined optimization model of the intelligent soft switch and the energy storage system is as follows:
Figure BDA0002534726970000041
wherein N istNumber of time sections, omegabIs the set of all branches of the distribution system, rijIs the resistance value of branch ij, It,ijFor the current amplitude of branch ij at time t,
Figure BDA0002534726970000042
for the loss of the intelligent soft switch installed on branch ij at time t,
Figure BDA0002534726970000043
the price of electricity at time t.
Optionally, the solving unit is specifically configured to:
decoupling the intelligent soft switch and the energy storage system joint interval optimization model to obtain a main problem model and a sub problem model;
and solving the main problem model and the sub problem model based on a column and constraint generation algorithm to obtain an optimization result.
According to the technical scheme, the method has the following advantages:
the application provides an intelligent soft switch and energy storage system combined optimization method, which comprises the following steps: acquiring system parameters of a power distribution system; establishing a deterministic combined optimization model of an intelligent soft switch and an energy storage system of a power distribution system based on system parameters; performing second-order cone model conversion on the deterministic combined optimization model of the intelligent soft switch and the energy storage system to obtain a deterministic second-order cone planning model of the intelligent soft switch and the energy storage system of the power distribution system; configuring an uncertain set of the output and load demand prediction of the distributed power supply, and constructing an intelligent soft switch and energy storage system combined interval optimization model based on an intelligent soft switch and energy storage system deterministic second-order cone programming model and the uncertain set; and solving the optimization model of the intelligent soft switch and energy storage system combined interval to obtain an optimization result, wherein the optimization result at least comprises a day-ahead scheduling strategy and an intelligent soft switch day-in-day operation strategy of the energy storage system.
According to the method for jointly optimizing the intelligent soft switch and the energy storage system, the energy storage system can store and release electric energy to realize the transfer of the electric energy in time, the impact on a power distribution system caused by the conditions of distributed power supply output fluctuation, load uncertainty and the like is effectively inhibited, the quick response capability of the intelligent soft switch is specific in real time, and the performance of the intelligent soft switch and the energy storage system can be maximally exerted by matching with a charge and discharge control strategy of the energy storage system, so that a deterministic joint optimization model of the intelligent soft switch and the energy storage system of the power distribution system is constructed on the basis of obtaining system parameters; performing second-order cone model conversion on the joint optimization model to obtain a deterministic second-order cone planning model, and then configuring an uncertain set of distributed power output and load demand prediction to further construct a joint interval optimization model; the optimization method comprises the steps of solving a joint interval optimization model to obtain an optimization result, configuring an uncertain set and constructing the joint interval optimization model, and considering uncertain factors into the model, so that the capability of coping with impact caused by the uncertain factors is improved, the operation safety of the power distribution system is further improved, and the technical problems that the existing intelligent soft switch operation optimization method is mostly used for predicting information about output of a deterministic distributed power supply and load requirements, the uncertain factors are less involved in processing, the impact caused by the uncertain factors cannot be coped, and the operation safety of the power distribution system is lower are solved.
Drawings
Fig. 1 is a schematic flowchart of a method for jointly optimizing an intelligent soft switch and an energy storage system according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an intelligent soft switching and energy storage system joint optimization device according to an embodiment of the present application;
FIG. 3 is a block diagram of an example of an improved IEEE 33 node provided by an embodiment of the present application;
FIG. 4 is a schematic diagram of a forecast curve of a day ahead load demand provided by an embodiment of the present application;
FIG. 5 is a schematic diagram of a power output prediction curve of a distributed power supply of the present application at a previous date according to an embodiment of the present application;
fig. 6 is a schematic diagram of a future scheduling strategy of the energy storage system according to an embodiment of the present application;
fig. 7 is a schematic diagram of an active power scheduling policy in an intelligent soft-switching intra-day scheduling policy provided in an embodiment of the present application;
fig. 8 is a schematic diagram of a reactive power scheduling policy in an intelligent soft-switching intra-day scheduling policy provided in an embodiment of the present application;
fig. 9 is a schematic flowchart of the joint optimization of the intelligent soft switching and energy storage system based on the column and constraint generation algorithm according to the embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For easy understanding, please refer to fig. 1, an embodiment of a method for jointly optimizing an intelligent soft switch and an energy storage system provided by the present application includes:
step 101, obtaining system parameters of a power distribution system.
The system parameters in the embodiment of the present application include: the system comprises line parameters, load levels, network topology connection relations, installation positions and capacities of distributed power supplies, installation positions and capacities of energy storage systems, system operation voltage levels and branch circuit current limits, access positions of intelligent soft switches, configuration capacities and loss coefficients, a day-ahead load demand prediction curve, a day-ahead distributed power supply output prediction curve, time-of-use electricity price parameters, uncertain regulation coefficients and uncertain deviations, and initial values of system reference voltages and reference powers.
And 102, constructing a deterministic combined optimization model of the intelligent soft switch and the energy storage system of the power distribution system based on the system parameters.
In the embodiment of the application, a deterministic combined optimization model of an intelligent soft switch and an energy storage system of a power distribution system is constructed based on system parameters, the deterministic combined optimization model of the intelligent soft switch and the energy storage system takes the minimum loss cost of the power distribution system as an objective function, and considers a network topology constraint, a system power flow constraint, a distributed power supply operation constraint, a power distribution system operation constraint, an intelligent soft switch operation constraint and an energy storage system operation constraint, wherein the objective function is as follows:
Figure BDA0002534726970000071
wherein N istNumber of time sections, omegabIs the set of all branches of the distribution system, rijIs the resistance value of branch ij, It,ijFor the current amplitude of branch ij at time t,
Figure BDA0002534726970000072
for the loss of the intelligent soft switch installed on branch ij at time t,
Figure BDA0002534726970000073
the price of electricity at time t.
The network topology constraints can be expressed as:
αt,ij＝βt,ij+βt,ji,ij∈Ωb(2)
Figure BDA0002534726970000074
Figure BDA0002534726970000075
αt,ij∈{0,1},ij∈Ωb(5)
βt,ij∈{0,1},ij∈Ωb(6)
in the formula, omeganIs the set of all nodes, Ω, in the distribution system0For a collection of source nodes in a power distribution system, αt,ijFor the switching state of branch ij at time t, αt,ijBranch ij is in the open state at time t, αt,ijBranch ij is in closed state at time t, β ═ 1t,ijFor the relationship between node i and node j at time t, the subscripts ij and ji denote the flow direction of the branch circuit, β t,ij1 denotes that at time t, node i is the parent of node j, β t,ij0 means that node i is not the parent of node j at time t.
The system flow constraint may be expressed as:
Figure BDA0002534726970000076
Figure BDA0002534726970000077
Figure BDA0002534726970000078
Figure BDA0002534726970000079
Figure BDA00025347269700000710
Figure BDA00025347269700000711
in the formula, rij、xijResistance and reactance, U, of branch ij, respectivelyt,iIs the voltage amplitude of node I at time t, It,ijThe current amplitude, P, of branch ij at time tt,ijFor the active power, P, flowing from node i to node j on the branch at time tt,jiFor the active power, Q, flowing to node i at node j on the branch at time tt,ijFor the reactive power, P, flowing from node i to node j on the branch at time tt,iFor the active power injected at node i at time t,
Figure BDA0002534726970000081
the active power injected for the distributed power supply at node i at time t,
Figure BDA0002534726970000082
for intelligent soft switching on node i at time tThe active power to be injected is turned off,
Figure BDA0002534726970000083
active power, Q, consumed by the load on time node it,iFor the reactive power injected at node i at time t,
Figure BDA0002534726970000084
the reactive power injected by the distributed power supply at node i for time t,
Figure BDA0002534726970000085
the reactive power injected for the intelligent soft switch at node i at time t,
Figure BDA0002534726970000086
the reactive power consumed by the load on node i at time instant.
The distributed power source operation constraints can be expressed as:
Figure BDA0002534726970000087
Figure BDA0002534726970000088
in the formula (I), the compound is shown in the specification,
Figure BDA0002534726970000089
the active power injected for the distributed power supply at node i at time t,
Figure BDA00025347269700000810
the reactive power injected by the distributed power supply at node i for time t,
Figure BDA00025347269700000811
for the distributed power supply output coefficient at the node i at the time t,
Figure BDA00025347269700000812
for the installed capacity of the distributed power supply on node i,
Figure BDA00025347269700000813
is the distributed power factor angle on node i.
The power distribution system operating constraints may be expressed as:
Figure BDA00025347269700000814
Figure BDA00025347269700000815
in the formula (I), the compound is shown in the specification,
Figure BDA00025347269700000816
respectively is an upper limit and a lower limit of the voltage of the operation node of the power distribution system,
Figure BDA00025347269700000817
for maximum operating branch current value, U, of the distribution systemt,iIs the voltage amplitude of node I at time t, It,ijThe current amplitude of branch ij at time t.
The intelligent soft switch operating constraints can be expressed as:
Figure BDA00025347269700000818
Figure BDA00025347269700000819
Figure BDA00025347269700000820
Figure BDA00025347269700000821
Figure BDA00025347269700000822
in the formula (I), the compound is shown in the specification,
Figure BDA00025347269700000823
the active power injected for the intelligent soft switch at node i at time t,
Figure BDA00025347269700000824
the reactive power injected for the intelligent soft switch at node i at time t,
Figure BDA00025347269700000825
the loss of the intelligent soft switching converter connected to the nodes i and j at the time t respectively,
Figure BDA00025347269700000826
for the capacity of the intelligent soft switch installed on branch ij,
Figure BDA00025347269700000827
the loss coefficients of the intelligent soft switching converter connected to the nodes i and j are respectively.
The energy storage system operating constraints may be expressed as:
Figure BDA0002534726970000091
Figure BDA0002534726970000092
Figure BDA0002534726970000093
Figure BDA0002534726970000094
Figure BDA0002534726970000095
Figure BDA0002534726970000096
Figure BDA0002534726970000097
in the formula (I), the compound is shown in the specification,
Figure BDA0002534726970000098
respectively the charging power and the discharging power of the energy storage system on the node i at the time t,
Figure BDA0002534726970000099
respectively a lower limit and an upper limit of the charging power of the energy storage system on the node i,
Figure BDA00025347269700000910
respectively a lower limit and an upper limit of the discharge power of the energy storage system on the node i,
Figure BDA00025347269700000911
are respectively the charging and discharging decision variables of the energy storage system on the node i at the time t,
Figure BDA00025347269700000912
representing the energy storage system at node i in a charging state at time t,
Figure BDA00025347269700000913
representing that the energy storage system at node i is not in a charging state at time t,
Figure BDA00025347269700000914
represented by the energy storage system being in a discharge state at node i at time t,
Figure BDA00025347269700000915
represented by the fact that the energy storage system at node i is not in a discharged state at time t,
Figure BDA00025347269700000916
for the total power injected into node i by the energy storage system at node i at time t,
Figure BDA00025347269700000917
respectively the charging efficiency and the discharging efficiency of the energy storage system on the node i,
Figure BDA00025347269700000918
for the charge of the energy storage system at node i at time t,
Figure BDA00025347269700000919
respectively is the lower limit and the upper limit of the charge coefficient of the energy storage system on the node i,
Figure BDA00025347269700000920
τ is the unit time step for the configured capacity of the energy storage battery installed on node i.
And 103, performing second-order cone model conversion on the deterministic combined optimization model of the intelligent soft switch and the energy storage system to obtain a deterministic second-order cone planning model of the intelligent soft switch and the energy storage system of the power distribution system.
Secondary item in deterministic combined optimization model of intelligent soft switch and energy storage system
Figure BDA00025347269700000921
Respectively using U2,t,i、I2,t,ijInstead, the following linearized expression is obtained:
Figure BDA00025347269700000922
Figure BDA00025347269700000923
Figure BDA00025347269700000924
Figure BDA00025347269700000925
Figure BDA00025347269700000926
Figure BDA00025347269700000927
will constrain the conditional expression
Figure BDA00025347269700000928
Carrying out linearization and convex relaxation to obtain a second-order cone constraint formula:
Figure BDA0002534726970000101
carrying out convex relaxation on the loss and capacity constraint conditions of the intelligent soft switch to obtain a rotating cone constraint formula:
Figure BDA0002534726970000102
Figure BDA0002534726970000103
Figure BDA0002534726970000104
Figure BDA0002534726970000105
the equations (2) - (6), the equations (11) - (14), the equation (17) and the equations (22) - (39) form an intelligent soft switch and energy storage system deterministic second-order cone planning model of the power distribution system, and a compact form of the model can be determined based on the intelligent soft switch and energy storage system deterministic second-order cone planning model of the power distribution system:
Figure BDA0002534726970000106
s.t.Ax+Dy+Hz≥f (41)
Cz＝d0(42)
||Gz||2≤gTz (43)
wherein, x: ═ Pch,Pdis)TFor the operating strategy of the energy storage system, y: ═ (P)SOP,QSOP)TIn order to realize the operation strategy of the intelligent soft switch,
Figure BDA0002534726970000107
NNnumber of nodes of power distribution system, NtThe number of the time sections is the number of the time sections,
Figure BDA0002534726970000108
Figure BDA0002534726970000109
respectively the charging power and the discharging power of the energy storage system on the node i at the time t,
Figure BDA00025347269700001010
Figure BDA00025347269700001011
Figure BDA00025347269700001012
respectively, the active power and the reactive power of the intelligent soft switch on the node i at the time t, A, C, D, G, H are respectively system matrixes of the model, and z: (U: ═ is2,I2,P,Q,V)TFor power flow control variables, U2:＝(U2,t,i,t＝1,2,…Nt,i＝1,2,…,NN)，U2,t,iIs the square of the magnitude of the voltage at node I at time t, I2:＝(I2,t,i,j,t＝1,2,…Nt,i,j＝1,2,…,NN)，I2,t,ijIs the square of the current magnitude on branch ij at time t, P: ═ Pt,i,t＝1,2,…,Nt,i＝1,2,…,NN)，Q:＝(Qt,i,t＝1,2,…,Nt,i＝1,2,…,NN)，Pt,i、Qt,iThe active power and the reactive power injected at the node i at the time t, respectively, wherein: ═ Vt,i,t＝1,2,…,Nt,i＝1,2,…,NN)，Vt,iFor electricity at node i at time tThe pressure amplitude values b, c, e, f and g are coefficient vectors of the model respectively,
Figure BDA0002534726970000111
for active power prediction of distributed power sources and loads,
Figure BDA0002534726970000112
Figure BDA0002534726970000113
for the active power prediction value of the distributed power supply at the node i at the time t,
Figure BDA0002534726970000114
Figure BDA0002534726970000115
and the predicted value of the active power of the load on the node i at the moment t is obtained.
Equation (40) corresponds to objective function equation (29), equation (41) corresponds to constraint conditional equations (22) to (24), equation (27), and equations (33) to (34), equation (42) corresponds to constraint conditional equations (2) to (6), equations (11) to (14), equation (17), equations (25) to (26), and equations (28) to (32), and equation (43) corresponds to second-order cone constraint equation (35) and rotation cone constraint equations (36) to (39).
And step 104, configuring an uncertain set of the output of the distributed power supply and load demand prediction, and constructing an intelligent soft switch and energy storage system combined interval optimization model based on the intelligent soft switch and energy storage system deterministic second-order cone planning model and the uncertain set.
In the embodiment of the application, the output of the distributed power source accessed by each node in the power distribution system and the change of load demand prediction are configured to be limited in a box-type uncertain set W, that is:
Figure BDA0002534726970000116
wherein the content of the first and second substances,
Figure BDA0002534726970000117
the active power of the distributed power supply on the node i at the moment t respectivelyThe actual value of the active power of the load and the actual value of the real power of the load,
Figure BDA0002534726970000118
the deviations introduced by the distributed power supply and the uncertain load variation range on the node i at the time t respectively,DG、Land respectively adjusting parameters for uncertainty corresponding to the distributed power supply and the load.
Based on the box type uncertain set W, constructing an intelligent soft switch and energy storage system combined interval optimization model on the basis of an intelligent soft switch and energy storage system deterministic second-order cone planning model:
Figure BDA0002534726970000119
Figure BDA00025347269700001110
wherein the content of the first and second substances,
Figure BDA00025347269700001111
Figure BDA0002534726970000121
wherein Z (x, y, d) is the feasible domain of the power distribution system intelligent soft switching and energy storage system deterministic combined optimization model given a set of x, y and d.
And 105, solving the intelligent soft switch and energy storage system joint interval optimization model to obtain an optimization result, wherein the optimization result at least comprises an energy storage system day-ahead scheduling strategy and an intelligent soft switch day-in-time operation strategy.
Solving the optimization model of the intelligent soft switch and energy storage system joint interval to obtain an optimization result, wherein the optimization process can refer to fig. 9, and the specific steps can be as follows:
1. decoupling an intelligent soft switch and an energy storage system joint interval optimization model to obtain a main problem model and a sub-problem model, decoupling the intelligent soft switch and the energy storage system joint interval optimization model to obtain a main problem model and a sub-problem model, wherein the sub-problem models are respectively a 'worst' scene sub-problem model and a 'optimistic' scene sub-problem model, and the main problem model is as follows:
Figure BDA0002534726970000122
Figure BDA0002534726970000123
Figure BDA0002534726970000124
in the formula, the variable dm∈ W, k is the iterative solution times, zmIs the sub-problem variable introduced to the main problem at the mth iteration.
The "worst" scenario sub-problem model is:
Figure BDA0002534726970000125
s.t.Hz≥f-Ax*-Dy*(π) (53)
Cz＝d(λ) (54)
||Gz||2≤gTz(σ,μ) (55)
the dual theory is utilized to convert the minimization problem of the inner layer into the maximization problem of the dual, the maximization problem and the maximization of the outer side are combined in a coincidence mode, and finally the sub-problem of the worst scene can be equivalently changed into the maximization form as follows:
Figure BDA0002534726970000126
s.t.HTπ+CTλ+∑(GTσ+gμ)＝e (57)
||σ||2≤μ,π,μ≥0,d∈W (58)
in the formula, λ and σ are free variables, and the variable yDUALAnd the { pi, lambda, sigma and mu } is a dual variable of the original optimization model.
The "optimistic" scenario sub-problem model is:
Figure BDA0002534726970000131
s.t.Hz≥f-Ax*-Dy*(60)
Cz＝d (61)
||Gz||2≤gTz (62)
2. solving the main problem model and the sub problem model based on the column and constraint generation algorithm to obtain an optimized result, wherein the specific solving process is as follows:
1) setting a lower limit value LB ═ infinity, an upper limit value UB ═ infinity and an initial iteration number k ═ 1 of an intelligent soft switch and energy storage system combined interval optimization model;
2) solving a main problem model based on a day-ahead load demand prediction curve and a day-ahead distributed power supply output prediction curve to obtain an optimal solution
Figure BDA0002534726970000132
Sum optimum value
Figure BDA0002534726970000133
And updating the lower limit value
Figure BDA0002534726970000134
3) Iterating the k word to obtain the optimal solution of the main problem model
Figure BDA0002534726970000135
Solving the "worst" scenario as a model of known sub-problems each substituted into
Figure BDA0002534726970000136
And its corresponding sub-problem optimal value fpes,kAnd "optimistic" scenario
Figure BDA0002534726970000137
And its corresponding subproblemsOptimum value fopt,kAnd updating the upper limit value
Figure BDA0002534726970000138
4) When UB-LB is less than or equal to the preset convergence threshold, the optimal energy storage system day-ahead scheduling strategy is found
Figure BDA0002534726970000139
Entering step 5); when UB-LB > is greater, let
Figure BDA00025347269700001310
Introducing variable zk+1And relevant constraints thereof are restricted in the main problem, k is updated to k +1, and the step 2) is returned;
5) the energy storage system day-ahead scheduling strategy obtained according to the step 4)
Figure BDA00025347269700001311
Solving the main problem again based on the daily load demand prediction curve and the daily distributed power output prediction curve to obtain an intelligent soft switch daily operation strategy
Figure BDA00025347269700001312
6) Intelligent soft switch in-day operation strategy obtained based on calculation
Figure BDA00025347269700001313
And energy storage system day-ahead scheduling strategy
Figure BDA00025347269700001314
And calculating and outputting corresponding loss cost.
According to the method for jointly optimizing the intelligent soft switch and the energy storage system, the energy storage system can store and release electric energy to realize the transfer of the electric energy in time, impact on a power distribution system caused by conditions such as distributed power supply output fluctuation and load uncertainty is effectively inhibited, the quick response capability of the intelligent soft switch is specific in real time, and the performance of the intelligent soft switch and the energy storage system can be maximally exerted by matching with a charge and discharge control strategy of the energy storage system, so that a deterministic joint optimization model of the intelligent soft switch and the energy storage system of the power distribution system is constructed on the basis of obtaining system parameters; performing second-order cone model conversion on the joint optimization model to obtain a deterministic second-order cone planning model, and then configuring an uncertain set of distributed power output and load demand prediction to further construct a joint interval optimization model; the optimization method comprises the steps of solving a joint interval optimization model to obtain an optimization result, configuring an uncertain set and constructing the joint interval optimization model, and considering uncertain factors into the model, so that the capability of coping with impact caused by the uncertain factors is improved, the operation safety of the power distribution system is further improved, and the technical problems that the existing intelligent soft switch operation optimization method is mostly used for predicting information about output of a deterministic distributed power supply and load requirements, the uncertain factors are less involved in processing, the impact caused by the uncertain factors cannot be coped, and the operation safety of the power distribution system is lower are solved.
Referring to fig. 3 to fig. 8, the present application further provides an embodiment of a method for jointly optimizing an intelligent soft switch and an energy storage system, including:
referring to an improved IEEE 33 node mathematical example structure diagram provided in fig. 3, first, the impedance value of the line element in the IEEE 33 node mathematical example, the active power and the reactive power of the load element, the network topology connection relationship, and the detailed parameters may refer to tables 1 and 2, in the mathematical example structure diagram in fig. 3, nodes 7 and 27 are connected to two sets of distributed power supplies, and the capacities are both 1000 kVA; a group of intelligent soft switches is connected between the node 12 and the node 22, the capacity is 1000kVA, and the loss coefficient is 0.02; the nodes 10 and 30 are connected into two groups of energy storage systems, and specific parameters are detailed in a table 3; the load demand prediction curve before the day is shown in detail in FIG. 4, the distributed power output prediction curve before the day is shown in FIG. 5, and the time-of-use electricity price parameters are shown in Table 4; the uncertain regulation coefficient of the distributed power supply is 2, the uncertain deviation is plus or minus 20 percent, namely the accessed distributed power supply can reach the upper limit or the lower limit of the deviation; the uncertain regulation coefficient of the load is 6, the uncertain deviation is +/-10 percent, namely 6 load nodes in 32 load nodes can reach the upper limit or the lower limit of the deviation, and the rest are processed according to the reference value; and finally, setting the reference voltage of the system to be 10kV and the reference power to be 1 MVA.
TABLE 1 IEEE 33 node sample load Access location and Power
Figure BDA0002534726970000141
Figure BDA0002534726970000151
TABLE 2 IEEE 33 node example line parameters
Figure BDA0002534726970000152
Figure BDA0002534726970000161
TABLE 3 energy storage System parameters
Figure BDA0002534726970000162
TABLE 4 time of use price parameter
Time period Price of electricity/yuan
1:00-5:00 0.32
6:00-9:00 0.42
10:00-12:00 0.58
13:00-16:00 0.42
17:00-20:00 0.58
21:00-24:00 0.42
In the embodiment of the application, the computer hardware environment for executing the optimized calculation is Intel (R) Xeon (R) CPU E5-2609, the main frequency is 2.50GHz, and the memory is 16 GB; the software environment is a Windows 10 operating system, the loss cost interval of the power distribution system is calculated to be [637.6, 1057.5] yuan based on the system parameters, the energy storage system day-ahead scheduling strategy is shown in fig. 6, the intelligent soft switch is subjected to intra-day optimal scheduling on the basis of the energy storage system day-ahead scheduling, the loss cost of the power distribution system is 818.3 yuan, and the intelligent soft switch intra-day scheduling strategy is shown in fig. 7 and fig. 8.
From the above results, it can be found that through the intelligent soft switch and energy storage system joint interval optimization model in the embodiment of the present application, a power distribution system loss cost interval of [637.6, 1057.5] yuan and an interval width of 419.9 yuan can be obtained, and through intra-day scheduling of the intelligent soft switch, a power distribution system loss cost of 818.3 yuan, which is within the cost interval, is obtained; observing a charge and discharge strategy of the energy storage system, wherein the energy storage system is in a discharge state in the peak period (10:00-12:00, 17:00-10:00) of the electricity price, and the charge and discharge strategy of the energy storage system based on the time-of-use electricity price is beneficial to the economic operation of a power distribution system and improves the operation safety of the power distribution system; the intelligent soft switch and energy storage system combined optimization method based on interval optimization can consider the influence of uncertainty of distributed power supply and load demand on loss cost of an active power distribution network to form a cost interval, and provides an energy storage system and intelligent soft switch combined optimization method adaptive to uncertainty to provide scientific guidance suggestions for scheduling personnel.
For easy understanding, please refer to fig. 2, the present application provides an embodiment of an intelligent soft switching and energy storage system joint optimization apparatus, including:
an obtaining unit 201, configured to obtain a system parameter of a power distribution system;
the first construction unit 202 is configured to construct a deterministic combined optimization model of the intelligent soft switch and the energy storage system of the power distribution system based on the system parameters;
the conversion unit 203 is used for performing second-order cone model conversion on the deterministic combined optimization model of the intelligent soft switch and the energy storage system to obtain a deterministic second-order cone planning model of the intelligent soft switch and the energy storage system of the power distribution system;
the second construction unit 204 is configured to configure an uncertainty set for predicting the output of the distributed power supply and the load demand, and construct an intelligent soft switch and energy storage system joint interval optimization model based on the intelligent soft switch and energy storage system certainty second-order cone planning model and the uncertainty set;
and the solving unit 205 is configured to solve the optimization model of the intelligent soft switch and energy storage system joint interval to obtain an optimization result, where the optimization result at least includes a day-ahead scheduling policy of the energy storage system and a day-in-day operation policy of the intelligent soft switch.
As a further improvement, the objective function of the deterministic combined optimization model of the intelligent soft switch and the energy storage system is as follows:
Figure BDA0002534726970000171
wherein N istNumber of time sections, omegabIs the set of all branches of the distribution system, rijIs the resistance value of branch ij, It,ijFor the current amplitude of branch ij at time t,
Figure BDA0002534726970000172
for the loss of the intelligent soft switch installed on branch ij at time t,
Figure BDA0002534726970000173
the price of electricity at time t.
As a further improvement, the solving unit 205 is specifically configured to:
decoupling an optimization model of the intelligent soft switch and energy storage system joint interval to obtain a main problem model and a sub problem model;
and solving the main problem model and the sub problem model based on the column and constraint generation algorithm to obtain an optimization result.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. An intelligent soft switch and energy storage system combined optimization method is characterized by comprising the following steps:
acquiring system parameters of a power distribution system;
constructing a deterministic combined optimization model of the intelligent soft switch and the energy storage system of the power distribution system based on the system parameters;
performing second-order cone model conversion on the deterministic combined optimization model of the intelligent soft switch and the energy storage system to obtain a deterministic second-order cone planning model of the intelligent soft switch and the energy storage system of the power distribution system;
configuring an uncertain set of the output and load demand prediction of the distributed power supply, and constructing an intelligent soft switch and energy storage system combined interval optimization model based on the intelligent soft switch and energy storage system deterministic second-order cone programming model and the uncertain set;
and solving the optimization model of the intelligent soft switch and energy storage system combined interval to obtain an optimization result, wherein the optimization result at least comprises an energy storage system day-ahead scheduling strategy and an intelligent soft switch day-in-time operation strategy.
2. The method of claim 1, wherein the system parameters comprise: the system comprises line parameters, load levels, network topology connection relations, installation positions and capacities of distributed power supplies, installation positions and capacities of energy storage systems, system operation voltage levels and branch circuit current limits, access positions of intelligent soft switches, configuration capacities and loss coefficients, a day-ahead load demand prediction curve, a day-ahead distributed power supply output prediction curve, time-of-use electricity price parameters, uncertain regulation coefficients and uncertain deviations, and initial values of system reference voltages and reference powers.
3. The method for jointly optimizing intelligent soft switching and energy storage system according to claim 1, wherein the objective function of the deterministic joint optimization model of intelligent soft switching and energy storage system is as follows:
Figure FDA0002534726960000011
wherein N istNumber of time sections, omegabIs the set of all branches of the distribution system, rijIs the resistance value of branch ij, It,ijFor the current amplitude of branch ij at time t,
Figure FDA0002534726960000012
for the loss of the intelligent soft switch installed on branch ij at time t,
Figure FDA0002534726960000013
the price of electricity at time t.
4. The method for jointly optimizing intelligent soft switching and energy storage systems according to claim 3, wherein the constraint conditions of the objective function comprise: the system comprises a network topology constraint, a system power flow constraint, a distributed power supply operation constraint, a power distribution system operation constraint, an intelligent soft switch operation constraint and an energy storage system operation constraint.
5. The intelligent soft switching and energy storage system joint optimization method according to claim 4, wherein the deterministic second-order cone programming model of the intelligent soft switching and energy storage system of the power distribution system is as follows:
Figure FDA0002534726960000021
wherein, x: ═ Pch,Pdis)TFor the operating strategy of the energy storage system, y: ═ (P)SOP,QSOP)TIn order to realize the operation strategy of the intelligent soft switch,
Figure FDA0002534726960000022
NNnumber of nodes of power distribution system, NtThe number of the time sections is the number of the time sections,
Figure FDA0002534726960000023
Figure FDA0002534726960000024
respectively the charging power and the discharging power of the energy storage system on the node i at the time t,
Figure FDA0002534726960000025
Figure FDA0002534726960000026
Figure FDA0002534726960000027
respectively active power and reactive power of the intelligent soft switch on a node i at the time t, A, C, D, G, H respectively a system matrix of a model, z:＝(U2,I2,P,Q,V)TFor power flow control variables, U2:＝(U2,t,i,t＝1,2,…Nt,i＝1,2,…,NN)，U2,t,iIs the square of the magnitude of the voltage at node I at time t, I2:＝(I2,t,i,j,t＝1,2,…Nt,i,j＝1,2,…,NN)，I2,t,ijIs the square of the current magnitude on branch ij at time t, P: ═ Pt,i,t＝1,2,…,Nt,i＝1,2,…,NN)，Q:＝(Qt,i,t＝1,2,…,Nt,i＝1,2,…,NN)，Pt,i、Qt,iThe active power and the reactive power injected at the node i at the time t, respectively, wherein: ═ Vt,i,t＝1,2,…,Nt,i＝1,2,…,NN)，Vt,iB, c, e, f, g are the coefficient vectors of the model respectively,
Figure FDA0002534726960000028
for active power prediction of distributed power sources and loads,
Figure FDA0002534726960000029
Figure FDA00025347269600000210
for the active power prediction value of the distributed power supply at the node i at the time t,
Figure FDA00025347269600000211
Figure FDA00025347269600000212
and the predicted value of the active power of the load on the node i at the moment t is obtained.
6. The intelligent soft switching and energy storage system joint optimization method of claim 5, wherein the uncertain set of distributed power output and load demand predictions is:
Figure FDA00025347269600000213
wherein the content of the first and second substances,
Figure FDA00025347269600000214
respectively the actual value of the active power of the distributed power supply at the node i at the time t and the actual value of the active power of the load,
Figure FDA00025347269600000215
the deviations introduced by the distributed power supply and the uncertain load variation range on the node i at the time t respectively,DG、Land respectively adjusting parameters for uncertainty corresponding to the distributed power supply and the load.
7. The method for jointly optimizing the intelligent soft switch and the energy storage system according to claim 1, wherein solving the optimization model of the joint interval of the intelligent soft switch and the energy storage system to obtain an optimization result comprises:
decoupling the intelligent soft switch and the energy storage system joint interval optimization model to obtain a main problem model and a sub problem model;
and solving the main problem model and the sub problem model based on a column and constraint generation algorithm to obtain an optimization result.
8. An intelligent soft switch and energy storage system combined optimization device is characterized by comprising:
the acquisition unit is used for acquiring system parameters of the power distribution system;
the first construction unit is used for constructing a deterministic combined optimization model of the intelligent soft switch and the energy storage system of the power distribution system based on the system parameters;
the conversion unit is used for performing second-order cone model conversion on the intelligent soft switch and energy storage system certainty combined optimization model to obtain an intelligent soft switch and energy storage system certainty second-order cone planning model of the power distribution system;
the second construction unit is used for configuring an uncertain set of the output and load demand prediction of the distributed power supply and constructing an intelligent soft switch and energy storage system joint interval optimization model based on the intelligent soft switch and energy storage system deterministic second-order cone planning model and the uncertain set;
and the solving unit is used for solving the optimization model of the intelligent soft switch and energy storage system combined interval to obtain an optimization result, and the optimization result at least comprises a day-ahead scheduling strategy and an intelligent soft switch day-in-day operation strategy of the energy storage system.
9. The intelligent soft-switching and energy-storage-system joint optimization device according to claim 8, wherein an objective function of the intelligent soft-switching and energy-storage-system deterministic joint optimization model is:
Figure FDA0002534726960000031
wherein N istNumber of time sections, omegabIs the set of all branches of the distribution system, rijIs the resistance value of branch ij, It,ijFor the current amplitude of branch ij at time t,
Figure FDA0002534726960000032
for the loss of the intelligent soft switch installed on branch ij at time t,
Figure FDA0002534726960000033
the price of electricity at time t.
10. The intelligent soft switching and energy storage system joint optimization device of claim 8, wherein the solving unit is specifically configured to:
decoupling the intelligent soft switch and the energy storage system joint interval optimization model to obtain a main problem model and a sub problem model;
and solving the main problem model and the sub problem model based on a column and constraint generation algorithm to obtain an optimization result.
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